MFDD-CP-ϕOTDR self-correction for fiber calibration in dynamic enviroments

Multi-Frequency Database Demodulation (MFDD) combined with chirped-pulse (CP) phase-sensitive OTDR (ϕ-OTDR) has recently emerged as a promising technique for distributed acoustic sensing (DAS), offering high long-term stability by mitigating 1/f noise without compromising sensitivity, and increasing...

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Detalles Bibliográficos
Autores: Vidal Moreno, Pedro Jose|||0000-0003-1123-7073, Barriopedro, Marcos G., Rosado, Alejandro, Hernández Martín, Laura, Martín López, Sonia|||0000-0001-5203-6206, Fidalgo Martins, Hugo|||0000-0003-3927-8125
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:dnet:ebuahbibliot::c427fd8f5e9dba53e999bd8a4ffe1963
Acceso en línea:http://hdl.handle.net/10017/69136
https://dx.doi.org/10.1109/JLT.2026.3678165
Access Level:acceso abierto
Palabra clave:MFDD-CP-ϕOTDR
Distributed acoustic sensing
Calibrated
Fiber optic sensing
Noise compensation
Electrónica
Electronics
Descripción
Sumario:Multi-Frequency Database Demodulation (MFDD) combined with chirped-pulse (CP) phase-sensitive OTDR (ϕ-OTDR) has recently emerged as a promising technique for distributed acoustic sensing (DAS), offering high long-term stability by mitigating 1/f noise without compromising sensitivity, and increasing acoustic bandwidth and dynamic range. However, a critical limitation has been the requirement for the fiber to remain unperturbed during the acquisition of the reference matrix, restricting practical deployment in environments with unavoidable ambient vibrations or temperature changes. In this work, we introduce and experimentally validate a self-correcting MFDD method that enables accurate reference matrix calibration under perturbations by treating the MFDD reference acquisition itself as a CP-ϕ-OTDR measurement. This approach allows perturbations occurring during calibration to be measured and compensated in post-processing, preserving the system's accuracy and stability. We demonstrate the method across three scenarios: (i) a baseline measurement with calibration under quiet conditions, (ii) calibration with intentionally applied perturbations later corrected in post-processing, and (iii) a long-term measurement over 35 hours to assess extended stability. The results show that the self-corrected MFDD approach maintains comparable performance in both fast (100 Hz) dynamic and quas-static measurements. This advancement significantly enhances the robustness of MFDD-CP-ϕ-OTDR systems, facilitating reliable long-term distributed sensing in practical environments for applications in geophysical monitoring, structural health assessment, and infrastructure surveillance.